From f31b8dd19fb285bccd4b85d7938186adafdf4788 Mon Sep 17 00:00:00 2001
From: ccamp104 <ccamp104@dsailogin.mgmt.ai.cluster>
Date: Fri, 2 Jan 2026 17:22:15 -0500
Subject: [PATCH] if dat_file is inputted and formula is not, have formula not
 be injected into prompt if filename is not a formula

---
 atomgpt/inverse_models/inverse_predict.py | 73 ++++++++++++++++++-----
 1 file changed, 58 insertions(+), 15 deletions(-)

diff --git a/atomgpt/inverse_models/inverse_predict.py b/atomgpt/inverse_models/inverse_predict.py
index 63054cf..da46085 100644
--- a/atomgpt/inverse_models/inverse_predict.py
+++ b/atomgpt/inverse_models/inverse_predict.py
@@ -12,6 +12,7 @@
 from ase.constraints import ExpCellFilter
 import time
 from jarvis.core.atoms import ase_to_atoms
+import re  # CHANGE: used to detect formula-like filenames
 
 parser = argparse.ArgumentParser(
     description="Atomistic Generative Pre-trained Transformer"
@@ -111,6 +112,9 @@ def predict(
     load_in_4bit=None,  # temp_config["load_in_4bit"]
     verbose=True,  # temp_config["load_in_4bit"]
 ):
+    # Track whether the user provided a formula via CLI
+    user_provided_formula = formula is not None
+
     print("config_path", config_path)
     if output_dir is not None:
         config_name = os.path.join(output_dir, "config.json")
@@ -144,6 +148,8 @@ def predict(
         print("Model used:", model_name)
         print("config used:", config_path)
         print("formula:", formula)
+        print("dat_path:", dat_path)
+
     model = None
     tokenizer = None
     try:
@@ -163,13 +169,18 @@ def predict(
             model_name, gguf_file=filename
         )
         pass
+
     atoms_arr = []
     lines = []
+
+    # If formula isn't given but dat_path is, run single-file inference (no pred_csv needed)
     if formula is None:
-        # if dat_path is None:
-        f = open(pred_csv, "r")
-        lines = f.read().splitlines()
-        f.close()
+        if dat_path is not None:
+            lines = [dat_path]
+        else:
+            f = open(pred_csv, "r")
+            lines = f.read().splitlines()
+            f.close()
     else:
         if dat_path is not None:
             lines = [dat_path]
@@ -177,6 +188,9 @@ def predict(
 
     mem = []
 
+    # CHANGE: simple heuristic for "formula-like" strings (e.g., LaB6, SiO2)
+    formula_like_re = re.compile(r"^([A-Z][a-z]?\d*)+$")
+
     for i in lines:
         prompt = i
         if ".dat" in i or dat_path is not None:
@@ -185,6 +199,11 @@ def predict(
                 fname_csv = os.path.join(parent, i)
             else:
                 fname_csv = dat_path
+
+            # Determine whether the DAT filename itself looks like a chemical formula
+            fname_stem = Path(fname_csv).stem
+            filename_looks_like_formula = bool(formula_like_re.fullmatch(fname_stem))
+
             _formula, x, y = load_exp_file(
                 filename=fname_csv,
                 intvl=intvl,
@@ -198,17 +217,40 @@ def predict(
                     formula = str(_formula.split("/")[-1].split(".dat")[0])
             except Exception:
                 pass
-            prompt = (
-                "The chemical formula is "
-                + formula
-                + " The "
-                + temp_config["prop"]
-                + " is "
-                + y_new_str
-                + ". Generate atomic structure description with lattice lengths, angles, coordinates and atom types."
-            )
+
+            # CHANGE: inject formula sentence only if:
+            #   - user provided --formula, OR
+            #   - DAT filename itself is a formula (e.g., LaB6.dat)
+            if user_provided_formula and formula is not None:
+                prompt = (
+                    "The chemical formula is "
+                    + formula
+                    + " The "
+                    + temp_config["prop"]
+                    + " is "
+                    + y_new_str
+                    + ". Generate atomic structure description with lattice lengths, angles, coordinates and atom types."
+                )
+            elif (not user_provided_formula) and filename_looks_like_formula:
+                prompt = (
+                    "The chemical formula is "
+                    + fname_stem
+                    + " The "
+                    + temp_config["prop"]
+                    + " is "
+                    + y_new_str
+                    + ". Generate atomic structure description with lattice lengths, angles, coordinates and atom types."
+                )
+            else:
+                prompt = (
+                    "The "
+                    + temp_config["prop"]
+                    + " is "
+                    + y_new_str
+                    + ". Generate atomic structure description with lattice lengths, angles, coordinates and atom types."
+                )
         else:
-            if formula is not None:
+            if user_provided_formula and formula is not None:
                 prompt = (
                     "The chemical formula is "
                     + formula
@@ -239,7 +281,6 @@ def predict(
             info["prompt"] = prompt
             info["error"] = "Invalid structure returned by AtomGPT (None)."
             mem.append(info)
-            # skip the rest of the loop for this entry
             continue
 
         if verbose:
@@ -255,6 +296,7 @@ def predict(
         info["prompt"] = prompt
         info["atoms"] = gen_mat.to_dict()
         mem.append(info)
+
     dumpjson(data=mem, filename=fname)
     return model, tokenizer, temp_config
 
@@ -273,3 +315,4 @@ def predict(
         prop_val=args.prop_val,
         background_subs=args.background_subs,
     )
+